论文标题
无源无监督的域适应性，具有标准和形状约束，用于医学图像分割
Source-Free Unsupervised Domain Adaptation with Norm and Shape Constraints for Medical Image Segmentation
论文作者
论文摘要
无监督的域适应性（UDA）是解决一个问题的关键技术之一，很难获得监督学习所需的地面真相标签。通常，UDA假设在训练过程中可用来源和目标域中的所有样本。但是，在涉及数据隐私问题的应用下，这不是现实的假设。为了克服这一限制，最近提出了无源无源数据的UDA，即无源无监督的域适应性（SFUDA）。在这里，我们提出了一种用于医疗图像分割的SFUDA方法。除了在UDA中常用的熵最小化方法外，我们还引入了一个损失函数，以避免目标域中的特征规范和在保留目标器官的形状约束之前。我们使用包括多种类型的源目标域组合的数据集进行实验，以显示我们方法的多功能性和鲁棒性。我们确认我们的方法优于所有数据集中的最先进。
Unsupervised domain adaptation (UDA) is one of the key technologies to solve a problem where it is hard to obtain ground truth labels needed for supervised learning. In general, UDA assumes that all samples from source and target domains are available during the training process. However, this is not a realistic assumption under applications where data privacy issues are concerned. To overcome this limitation, UDA without source data, referred to source-free unsupervised domain adaptation (SFUDA) has been recently proposed. Here, we propose a SFUDA method for medical image segmentation. In addition to the entropy minimization method, which is commonly used in UDA, we introduce a loss function for avoiding feature norms in the target domain small and a prior to preserve shape constraints of the target organ. We conduct experiments using datasets including multiple types of source-target domain combinations in order to show the versatility and robustness of our method. We confirm that our method outperforms the state-of-the-art in all datasets.
